Despite gender differences associated with diabetes incidence, some studies have proposed that BMI2,4,10,11,12WC2,3,11HC1 and WHR2,10,20 might be useful indices to predict adiposity, cardiovascular risk factors and metabolic syndrome. Studies have reported an association between high VF and the risk of diabetes complications, cardiometabolic diseases, and metabolic abnormalities in diabetic patients2,3,4,10; hence, assessing an individual’s body composition using appropriate indices is significant.
The findings of the present study showed that BMI, WC, HC, WHR and WHtR were statistically associated with VF levels determined by BIA among diabetic patients. The findings are useful when considering the burden of VF. Second, in the absence of BIA in low-resource settings for clinical practice and epidemiological studies, these anthropometric indices might be valuable for assessing VF levels. However, the regression analysis of the present study showed that WHtR [UOR = 21.49, p < 0.001] might be a better anthropometric index for identifying diabetic patients with high VF levels than BMI [UOR = 6.77, p = 0.008]WC [UOR = 6.37, p < 0.001]HC [UOR = 5.93, p = 0.002] and WHR [UOR = 13.17, p < 0.001] (see Table 2).
Likewise, the ROC curves of the anthropometric indices confirmed that WHtR showed the greatest predictive capacity of [AUC = 0.745, p ˂0.001] compared to BMI [AUC = 0.584, p = 0.047]WC [AUC = 0.723, p ˂0.001]HC [AUC = 0.647, p ˂0.001] and [AUC = 0.711, p ˂0.001] in identifying diabetic patients with high VF levels (see Table 3 and Fig. 1). The findings are innovative and added to the literature. For the first time, this study revealed the accuracy of using WHtR in identifying diabetic patients with high VF levels. It is, however, inconsistent with a study performed in Ghana by Eghan et al.2who reported that both WC and HC had the largest ROC value [AUC = 0.79] in estimating VF among type II diabetic patients (T2DM) compared to BMI [AUC = 0.67]WHR [AUC = 0.53] and tricips skinfold thickness [AUC = 0.58]. The discrepancy could be the study setting and methodology.
There are inadequate studies performed for direct comparisons; However, many studies3,4,10,20,21,26,27,28including two meta-analyses17,18 conducted globally in different study populations and ethnicities, have reported WHtR as the best anthropometric index. Those studies have focused on determining the accuracy of using anthropometric indices to predict adiposity, cardiometabolic risk factors and metabolic syndrome 3,4,10,17,18,20,21,26,27,28. Similar to the present study, although not a straightforward comparison, Moosaie et al.4 and Pasdar et al.10 reported that WHtR had the highest ROC values [AUC = 0.61 and 0.69] in predicting cardiovascular disease in T2DM patients and the healthy Iranian population, respectively. Dou et al.29 reported that WHtR had the maximum ROC values [AUC = 0.84 and 0.88] to predict cardiometabolic risk in Chinese children males and females. Tee et al.27 reported that WHtR had the greatest ROC values [AUC = 0.78 and 0.82] in predicting high blood pressure among Malaysian adolescent boys and girls. Shrestha et al.28 reported that WHtR had the highest ROC value [AUC = 0.60] as the best screening tool for hypertension among the Nepali population. Bacopoulou et al.21 reported that WHtR had the utmost ROC value [AUC = 0.97] in predicting abdominal obesity among Greek adolescents.
In contrast, again not a direct comparison with the present study, a study performed on the Iran population by Tutunchi et al.22 reported that both WHtR and WC had the highest ROC values [AUC = 0.97] in predicting overweight and obesity compared with WHR [AUC = 0.79]. In the Chinese population, Zhang et al.3 reported that both WC and WHtR had the greatest ROC values [AUC = 0.67 and AUC = 0.68, respectively] in predicting diabetes risk in Chinese males and females compared to BMI [AUC = 0.63] and WHR [AUC = 0.65]. Hernández-Vásquez et al.14 found that the conicity index had the maximum ROC value [AUC = 0.67] as the best predictor for diabetes in Peruvian men and women. However, the Ministry of Health in Peru has endorsed the use of both BMI and WC for the assessment of adiposity14.
The likely reasons are as follows: first, individuals with shorter heights have remarkably greater quantities of body fat compared to taller heights with the same BMI4,14. Second, individuals with similar WCs but different heights do not have the same quantities of body fat3,4. Third, being short in state was associated with a higher accumulation of VF compared to being tall3,14. Additionally, studies have reported WC as a cardiometabolic risk factor compared to weight4,14. Finally, height alone predicts hypertension and diabetes, and the percentage of body fat associated with WC is an independent risk factor for cardiovascular disease.4.
Although BMI also includes height measurement in the calculation, it is unable to differentiate between body fat and lean mass2,3,4 compared to WHtR, which is more reflective of body fat, particularly VF2,3,4,10. Studies have shown that BMI independently contributes to the prediction of VF2,11,12, which coincided with the present study. Therefore, BMI might be a possible index in identifying diabetic patients with high VF levels; however, the association of BMI and VF [UOR = 6.77, p = 0.008] and the precision [AUC = 0.584, p = 0.047] in identifying diabetic patients with high VF levels at cut-off > 25.7 kg/m2 were lower compared to WHtR (see Table 2, Table 3 and Fig. 1). The WHO has proposed that the BMI cut-offs at which substantial cardiometabolic risk is established varies depending on the country10. A prior study suggested that different BMI cut‐off points must be reviewed and reintroduced among ethnic populations for better sensitivity and specificity10.
Many studies have emphasized that WC might be a better index for cardiometabolic risk factors3,4,10. Other studies have reported an inverse association between height and cardiometabolic risk factors leading to morbidity and mortality4,14. The association of WC with VF [UOR = 6.37, p < 0.001] and the exact [AUC = 0.723, p ˂0.001] in identifying diabetic patients with high VF levels at cut-offs > 80.5 cm were lower than those of WHtR (see Tables 2, 3 and Fig. 1). Therefore, WC might be a possible index in identifying diabetic patients with high VF; however, it ignores the effect of height; hence, it might underestimate and overestimate the levels of VF in shorter and taller people. Furthermore, Hernández-Vásquez et al.14 reported that height is very important in some populations, especially Peruvian, who have shorter heights worldwide. In Peruvian adults, WHtR has been shown to have the strongest association with hypertension in both genders14.
Eghan et al.2 reported HC to be a potential predictor of VF estimates in diabetic patients, which is consistent with the present study. However, there were low to moderate associations between HC and VF levels compared to WHtR (see Table 2). The association of HC and VF [UOR = 5.93, p = 0.002] and the relative ability [AUC = 0.647, p ˂0.001] to correctly identify diabetic patients with high VF levels at cut-off > 95.5 cm was good; however, that of WHtR was better (see Table 2, Table 3 and Fig. 1). WHtR incorporates the height of the individual in the calculation, hence increasing the accuracy of the estimation of risks14. Additionally, Moosaie et al.4 reported WHtR as a more accurate tool for predicting hypertension in patients with T2DM.
Other studies have acknowledged WHR association with VF estimates2 and diabetes3; however, Eghan et al.2 have called for further study to appraise the efficacy of WHR. At a cut-off > 0.82 cm, WHR produced a relatively high sensitivity (72.6%) and a weak specificity of (35.8%), with [AUC = 0.711, p ˂0.001] (see Table 3 and Fig. 1). This implies that diabetic patients with higher WHR than normal are likely to have more accumulation of VF, which is risky for their health2. Based on the present study, WHR might be a possible index in identifying diabetic patients with high VF; however, the greater AUC for WHtR compared to WHR and its usefulness in diverse ethnicities19,20 recommend it as a better index for predicting adiposity.
WHtR showed higher efficacy than the other four anthropometric indices in identifying diabetic patients with high VF levels. First, it overpowered most of the limitations of BMI, WC, HC and WHR when adjusted (see Table 2). Second, WHtR has been recommended in several studies3,4,10,20 and provides a cut-off value that is equally useful to both genders, ethnicities and different ages against other variables of abdominal adiposity3,20,21,26. Third, WHtR is shown to be a simple and reliable index to predict metabolic syndrome, cardiometabolic risk factors and adiposity at a cut-off > 0.5, which is the best index for identifying diabetic patients with high VF levels reported in the present study. These benefits have been brief in the following public health motto: “keep your waist circumference to less than half of your height”10,20. Finally, the advantages WHtR has over the four anthropometric indices are easiest to memorize for counseling patients, although it has no standard classification yet.
Strengths and limitations
The strengths of this study exist in the study population, and it provides information for further study. Second, the measurements of the anthropometric variables were carried out by trained research assistants through dual assessments per a standard protocol to reduce recall and social desirability bias. Nonetheless, this study has some limitations. First, the diabetic patients were enrolled from one region out of sixteen regions in Ghana and limited to the selected hospitals; Therefore, care should be taken when generalizing the findings. Second, information on diet, physical activity and lifestyle were not included in the analysis due to their scarcity; hence, adjusting for these covariates might affect the results of the regression analysis. Third, some of the biochemical and hemodynamic data were not available and recorded for all patients due to their appointment times. Furthermore, the five adiposity anthropometric indices were not analysed, presented and discussed according to gender using different cut-off values due to the insignificance of the analysed data according to gender. Finally, the use of BIA as the reference standard in this study was not classified according to gender9; Therefore, care should be taken when deducing and generalizing the findings to the population.